Optimized Artificial Neural Networks-Based Methods for Statistical Downscaling of Gridded Precipitation Data

Precipitation as a key parameter in hydrometeorology and other water-related applications always needs precise methods for assessing and predicting precipitation data. In this study, an effort has been conducted to downscale and evaluate a satellite precipitation estimation (SPE) product using artif...

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Veröffentlicht in:Water (Basel) 2019-08, Vol.11 (8), p.1653
Hauptverfasser: Salimi, Amir Hossein, Masoompour Samakosh, Jafar, Sharifi, Ehsan, Hassanvand, Mohammad Reza, Noori, Amir, von Rautenkranz, Hary
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container_end_page
container_issue 8
container_start_page 1653
container_title Water (Basel)
container_volume 11
creator Salimi, Amir Hossein
Masoompour Samakosh, Jafar
Sharifi, Ehsan
Hassanvand, Mohammad Reza
Noori, Amir
von Rautenkranz, Hary
description Precipitation as a key parameter in hydrometeorology and other water-related applications always needs precise methods for assessing and predicting precipitation data. In this study, an effort has been conducted to downscale and evaluate a satellite precipitation estimation (SPE) product using artificial neural networks (ANN), and to impose a residual correction method for five separate daily heavy precipitation events localized over northeast Austria. For the ANN model, a precipitation variable was the chosen output and the inputs were temperature, MODIS cloud optical, and microphysical variables. The particle swarm optimization (PSO), imperialist competitive algorithm,(ICA), and genetic algorithm (GA) were utilized to improve the performance of ANN. Moreover, to examine the efficiency of the networks, the downscaled product was evaluated using 54 rain gauges at a daily timescale. In addition, sensitivity analysis was conducted to obtain the most and least influential input parameters. Among the optimized algorithms for network training used in this study, the performance of the ICA slightly outperformed other algorithms. The best-recorded performance for ICA was on 17 April 2015 with root mean square error (RMSE) = 5.26 mm, mean absolute error (MAE) = 6.06 mm, R2 = 0.67, bias = 0.07 mm. The results showed that the prediction of precipitation was more sensitive to cloud optical thickness (COT). Moreover, the accuracy of the final downscaled satellite precipitation was improved significantly through residual correction algorithms.
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In this study, an effort has been conducted to downscale and evaluate a satellite precipitation estimation (SPE) product using artificial neural networks (ANN), and to impose a residual correction method for five separate daily heavy precipitation events localized over northeast Austria. For the ANN model, a precipitation variable was the chosen output and the inputs were temperature, MODIS cloud optical, and microphysical variables. The particle swarm optimization (PSO), imperialist competitive algorithm,(ICA), and genetic algorithm (GA) were utilized to improve the performance of ANN. Moreover, to examine the efficiency of the networks, the downscaled product was evaluated using 54 rain gauges at a daily timescale. In addition, sensitivity analysis was conducted to obtain the most and least influential input parameters. Among the optimized algorithms for network training used in this study, the performance of the ICA slightly outperformed other algorithms. The best-recorded performance for ICA was on 17 April 2015 with root mean square error (RMSE) = 5.26 mm, mean absolute error (MAE) = 6.06 mm, R2 = 0.67, bias = 0.07 mm. The results showed that the prediction of precipitation was more sensitive to cloud optical thickness (COT). 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subjects Accuracy
Algorithms
Datasets
Evolutionary algorithms
Floods
Gauges
Genetic algorithms
Hydrologic data
Hydrometeorology
Neural networks
Optical thickness
Optimization
Optimization algorithms
Precipitation
Rain gauges
Rainfall
Root-mean-square errors
Satellites
Sensitivity analysis
Sensors
Statistical methods
Variables
title Optimized Artificial Neural Networks-Based Methods for Statistical Downscaling of Gridded Precipitation Data
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